It is natural to compare upper bound for probability of a model to fail obtained via proposed method with the upper limit of corresponding Clopper-Pearson confidence interval [1]} for an unknown binomial probability \(p\) . Namely, we randomly sample \(n\)  transforms and count the number \(k\)  of the ones that mislead a classifier. If it is zero, we can upper bound the probability \({p \le \frac{C(\alpha )}{n}}\)  given confidence level \(\alpha \) . Thus, in order to get bound of \(10^{-4}\)  we need at least 10000 transformed version of a sample. Analogously to probabilistically certified accuracy, Clopper-Pearson certified accuracy (CPCA) is computed as
\(\text{CPCA}(\mathcal {S}, \varepsilon ) = \frac{\left| (x, y) \in \mathcal {S}: \texttt {CP}(x) < \varepsilon \ \&\ h(x)=y \right|}{m},\) 
